From the architecture described in the previous section, monitoring and eval- uating systems for plants are developed, integrating in the software of each plant components corresponding to those subsystems which are present. As example of developed systems, this section presents an application which has been devel- oped for the Agencia Municipal de la Energ´ıa del Ayuntamiento de M´alaga. The application allows remote monitoring and evaluation of 40 different solar power facilities, mostly located in public schools and government buildings. All facilities are located in M´alaga.
Inverter technologies present in the different plants include Sunways, Ingeteam, Mastervolt, SMA, PowerOne, SolarMax and Fronius. On each power plant floor
7.3. EXAMPLE SCENARIO 131 start Input: Recorded measures Estimate virtual measurements Yf,day(i) − Y ∗(i) f,day∈ [−1.96ˆσ, +1.96ˆσ] i = 0 (hours) Eh∗(i)− Eh(i)∈ [−1.96ˆσ, +1.96ˆσ] mark hour i i < total measures Operation problems detected No problems detected end no no yes yes yes
132 7. MONITORING PV PLANTS the existing monitoring system consists of a datalogger that depends on the char- acteristics of the inverter manufacturer (Meteocontrol, Solarlog, Mastervolt, etc.). With the objective of standardize the acquisition of data, OPC servers have been developed to allow standard access to available data on each technology to inte- grate them into the system.
The values obtained for serveral days without any detected problems are shown in Fig. 7.4.
Figure 7.4: Daily parameters
In figure 7.5 and 7.6 operational problems detected with developed software in two different plants are shown.
In the first one, connection problems detected in one of the inverters are shown, while in the second figure, joint analysis of operation of two inverters from the same power plant allows to detect a malfunction of one of them.
7.4
Conclusions
This chapter presents an assessing model for photovoltaic facilities. This model has been integrated in a monitoring framework for solar photovoltaic plants. With
7.4. CONCLUSIONS 133
Figure 7.5: Detecting problems in the inverter
the solution proposed applications that integrate a single tool monitoring photo- voltaic systems connected to network with inverters or data acquisition systems from different technologies, meter reading and production analysis procedures, evaluation, fault detection and alarm generation of the plant. This proposal enables evaluation of photovoltaic plants remotely using a single program, and without relying on software developed by inverters manufacturers. In addition, this assessment allows rapid action in a plant when malfunctions are detected in the same, thanks to the sending alerts system and remote access to plant data which facilitates maintenance tasks and increases the profitability of photovoltaic systems.
134 7. MONITORING PV PLANTS
135
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Chapter 8
Conclusions and future work
The aim of this thesis was to present new models to forecast hourly solar radi- ation that could be helpful for electricity energy plants and eletricity distribution network administrators in order to know beforehand how much energy will solar power plants produce in a short-term.
Two models are proposed to forecast hourly solar radiation. The first one is based on using the cumulative probability distribution functions to identify different types of days. The K-means algorithm is proposed to cluster samples so that each cluster represents one type of day. A method is described to get the hourly solar radiation profile for a day taking as input only the daily clearness index (Kd) of that day. Results show a good performance as the energy error
is 10.5% for all the data set, and a 5% for 57% of the energy. As a result of this analysis, it is possible to conclude that all estimated cumulative probability distribution functions can be summarized using six different types of curves. The results indicate that it is possible to use the obtained daily profiles of hourly solar radiation distribution to forecast the hourly values of this variable.
In the second proposed model, a new approach based on the use of different data mining techniques to model and to improve the next-day prediction of hourly global solar radiation has been developed. Modeling and prediction are conducted in two different phases and using different data mining techniques in each one. This second model introduces a new variable, which is related to the daily radiation profile and the daily clearness index. This variable is calculated for everyday and then K-means is applied to separate samples into clusters to identify different types of days.
138 8. CONCLUSIONS AND FUTURE WORK A clustering algorithm is used to identify how many different day types there are in the first phase, and in the second phase, different classification algorithms are combined with regression algorithms to obtain the parameters to forecast hourly global solar radiation with four method combinations: decision trees, artificial neural networks, support vector machine regression and support vector machine classification.
The classification algorithms are used to estimate the cluster to which the day belongs to. Regression algorithms are used to estimate the daily clearness index. These two estimated parameters and the proposed centroids are used to predict the hourly global solar radiation. Four different types of daily profiles have been estimated. These profiles correspond to clear (cloudless), overcast (variable among the day), overcast mainly in the morning and overcast mainly in the afternoon.
Regarding the results obtained in Experiment 1, when the independent vari- ables are the values of the meteorological parameters for the previous day, the rM AE value for the best model is 16.7% and the RM SE value 23.5%. The results obtained for rM AE and RM SE are 15.2% and 22.9% respectively for Ex- periment 2, when the independent variables are the forecasts of meteorological variables for the same day to be forecasted except for the value of daily clearness index that corresponds to the previous day. These values are inside the benchmark previously proposed by several authors.
The proposed model to forecast hourly solar global radiation using the fore- casted values of meteorological parameters has been used to assess photovoltaic facilities. This model has been integrated into a monitoring framework for solar photovoltaic plants. With the solution proposed applications that integrate a sin- gle tool monitoring photovoltaic systems connected to network with inverters or data acquisition systems from different technologies, meter reading and production analysis procedures, evaluation, fault detection and alarm generation of the plant. This proposal enables evaluation of photovoltaic plants remotely using a single program, and without relying on software developed by inverters manufacturers.
Finally, a prototype application is presented in order to estimate hourly solar raditation values from real data taken from AEMET (Agencia Estatal de Mete- orolog´ıa), which shows the usefulness of the second proposed model. This appli- cation obtains data from Aemet station as well as Junta de Andaluc´ıa stations spread throughout the entire Region.
Future work includes testing models with larger data sets and from different locations, and testing with input sets that do not contain radiation information
139 (Kd) because there are locations that do not have solar radiation measurement
equipment. Using more input variables can help to improve the forecasting ac- curacy because more variables may contain more information about atmosphere behaviour. Also, propose other machine learning models besides SVM, DT and ANN. Adaptative models are another type of model that could be investigated to improve results.
The developed or improved model could be incorporated to the existing mon- itoring system improving service to power plants administrators and users.
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